Bibliographic record
Abstract
The following conferences may be of interest to the readers of the Quarterly Review of Distance Education.“SITE 2019 in Las Vegas, Nevada, is the 30th annual conference of the Society for Information Technology and Teacher Education. This society represents individual teacher educators and affiliated organizations of teacher educators in all disciplines, who are interested in the creation and dissemination of knowledge about the use of information technology in teacher education and faculty/staff development.”http://site.aace.org/conf/“EdMedia + Innovate Learning, the premier international conference in the field since 1987, spans all disciplines and levels of education attracting researchers and practitioners in the field from 70+ countries. This annual conference offers a forum for the discussion and exchange of research, development, and applications on all topics related to Innovation and Education. EdMedia + Innovate Learning is an international conference organized by the Association for the Advancement of Computing in Education.”https://www.aace.org/conf/edmedia/about/“The 2019 AERA Annual Meeting, based in the vibrant multicultural city of Toronto and in the country of Canada, known more for political cooperation than partisan squabbles, is an opportunity to assess the state of education research. It is also an opportunity to explore how our work can help overcome the challenges of our time by becoming more relevant to communities, practitioners, and policy makers who believe in democratic principles and the public schools that should sustain those principles.”http://www.aera.net/Events-Meetings/Annual-Meeting“Our goal is simple: to provide you with a transformative professional development experience at an affordable price. This conference emphasizes evidence-based practice, educational innovation, and practical applications of theories and research findings in the field of distance education and online learning. There are also many opportunities for you to network, share, and collaborate with peers from around the world. You will head home with new skills, fresh knowledge, and the motivation to apply what you have learned.”https://dtlconference.wisc.edu/“The AECT International Convention brings together participants from around the world, offering practical applications, cutting-edge research, hands-on workshops, and demonstrations of new technologies in teaching and learning. This year’s convention theme is Inspired Professional Learning. Inspired Learning Professionals.”https://members.aect.org/events/call“E-Learn provides a unique forum for education and training professionals to discuss the latest research, development, applications, issues, and strategies, to explore new technologies, and to identify solutions for today’s challenges related to online learning. A variety of opportunities and venues are designed to enable participants to actively learn from and collaborate with a multinational, cross-industry expert faculty and peers on the research, development, diverse learning experiences, implementation and technology needed to improve e-learning. With 600+participants from 40+different countries typically attending this conference, E-Learn is one of the leading international e-learning conferences and gathering of e-learning and distance education professionals.”https://www.aace.org/conf/elearn/about/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.753 | 0.659 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".